NegativeBinomialDistribution Class

Represents the negative binomial distribution.

Definition

Namespace: Extreme.Statistics.Distributions
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
C# 
[SerializableAttribute]
public class NegativeBinomialDistribution : DiscreteDistribution
Inheritance
Object  →  Distribution  →  DiscreteDistribution  →  NegativeBinomialDistribution

Remarks

The negative binomial distribution models the number of failures before a specified number of successes in a series of Bernoulli trials. A Bernoulli trial is an experiment with two possible outcomes, labeled 'success' and 'failure,' where the probability of success has a fixed value for all trials.

A negative binomial distribution has two parameters: the NumberOfTrials and the ProbabilityOfSuccess of an individual trial.

Example

In jury selection, the total number of candidates before 12 jurors have been selected has a negative binomial distribution.

When playing a video game where the probability of completing a level is constant, the total number of levels completed before the three lives are used up has a negative binomial distribution.

Constructors

NegativeBinomialDistribution Constructs a new NegativeBinomialDistribution object.

Properties

Entropy Gets the entropy of the distribution.
(Inherited from Distribution)
IsUnimodal Gets whether the distribution has one or more modes.
(Inherited from DiscreteDistribution)
Kurtosis Gets the kurtosis of the distribution.
(Overrides Distribution.Kurtosis)
Mean Gets the mean or expectation value of the distribution.
(Overrides Distribution.Mean)
Mode Gets the mode of the distribution.
(Overrides DiscreteDistribution.Mode)
NumberOfModes Gets the number of modes of the distribution.
(Inherited from DiscreteDistribution)
NumberOfTrials Gets the number of trials.
ProbabilityOfSuccess Gets the probability that a trial is successful.
Skewness Gets the skewness of the distribution.
(Overrides Distribution.Skewness)
StandardDeviation Gets the standard deviation of the distribution.
(Inherited from Distribution)
StatisticSymbol Gets the common symbol to describe a statistic from the distribution.
(Inherited from Distribution)
Variance Gets the variance of the distribution.
(Overrides Distribution.Variance)

Methods

DistributionFunction Evaluates the cumulative distribution function of the distribution.
(Overrides DiscreteDistribution.DistributionFunction(Int32))
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
FinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object)
GetAllModes Returns an array that contains all the modes of the distribution.
(Inherited from DiscreteDistribution)
GetExpectedHistogram(Index<Interval<Int32>>, Double) Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
(Inherited from DiscreteDistribution)
GetExpectedHistogram(Index<Int32>, Double) Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
(Inherited from DiscreteDistribution)
GetExpectedHistogram(Int32, Int32, Double) Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
(Inherited from DiscreteDistribution)
GetHashCodeServes as the default hash function.
(Inherited from Object)
GetRandomSequence() Returns a sequence of random samples from the distribution.
(Inherited from DiscreteDistribution)
GetRandomSequence(Random) Returns a sequence of random samples from the distribution.
(Inherited from DiscreteDistribution)
GetRandomSequence(Random, Int32) Returns a sequence of random samples of the specified length from the distribution.
(Inherited from DiscreteDistribution)
GetTypeGets the Type of the current instance.
(Inherited from Object)
InverseDistributionFunction Returns the inverse of the distribution function.
(Inherited from DiscreteDistribution)
LeftTailProbability Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
(Inherited from DiscreteDistribution)
LogProbability Returns the logarithm of the probability of obtaining a specific integer value in the distribution.
(Overrides DiscreteDistribution.LogProbability(Int32))
MemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object)
Probability(Int32) Evaluates the probability function of the distribution.
(Overrides DiscreteDistribution.Probability(Int32))
Probability(Int32, Int32) Gets the probability of obtaining a sample that falls within the specified interval from the distribution.
(Inherited from DiscreteDistribution)
RightTailProbability Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
(Inherited from DiscreteDistribution)
Sample() Returns a random sample from the distribution.
(Inherited from DiscreteDistribution)
Sample(Int32) Returns a vector of random samples from the distribution.
(Inherited from DiscreteDistribution)
Sample(Random) Returns a random sample from the distribution.
(Overrides DiscreteDistribution.Sample(Random))
Sample(Int32, Random) Returns a vector of random samples from the distribution.
(Inherited from DiscreteDistribution)
Sample(Random, Int32[]) Fills an Int32 array with random numbers.
(Inherited from DiscreteDistribution)
Sample(Random, Int32, Double) Returns a single random sample from a negative binomial distribution with the specified parameters.
Sample(Random, Int32[], Int32, Int32) Fills an Int32 array with random numbers from this DiscreteDistribution.
(Inherited from DiscreteDistribution)
ToStringReturns a string that represents the current object.
(Overrides Object.ToString())
TwoTailProbability Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
(Inherited from DiscreteDistribution)

See Also